International trade can be beneficial for firms in terms of less expensive inputs for manufacturing and new markets for exporting finished products and services. Time spent waiting for imports and exports to clear customs can be costly for firms and deter them from engaging in foreign trade. The twelve indicators on this page measure the complexities of international trade in 139 countries. The results are based on surveys of more than 135,000 firms. A database query tool is available to help you better understand the overall prevalence of international trade, in addition to firms' individual experiences with international trade across various subgroups. You can also generate graphs to compare countries.

To see the details for a specific economy, click on the links below. Click on column headers to sort data.



Custom Data Set

Generate a Custom Data Set for Trade including standard errors, indicator values by firm subgroups, historical data and selected countries.


Days to clear direct exports through customsNumber of days to clear direct exports through customs.
Percent of firms exporting directly or indirectly (at least 10% of sales)Percent of firms that export directly or indirectly at least 10% of their total annual sales.
Percent of firms exporting directly (at least 10% of sales)Percent of firms exporting directly at least 10% of their sales.
Proportion of total sales that are exported directly (%)Percentage of total annual sales that are exported directly.
Days to clear imports from customs*Number of days to clear imports from customs.
Percent of firms using material inputs and/or supplies of foreign origin*Percent of firms that use material inputs and/or supplies of foreign origin.
All Countries 27.415.811.15.811.861.4
East Asia & Pacific7.
Europe & Central Asia4.520.
Latin America & Caribbean7.914.
Middle East & North Africa6.421.216.48.410.062.5
South Asia8.712.
Sub-Saharan Africa10.
Afghanistan (2014)
Albania (2013)
Angola (2010)
Antigua and Barbuda (2010)6.423.917.17.0n.a.64.6
Argentina (2017)
Armenia (2013)
Azerbaijan (2013)n.a.
Bahamas, The (2010)6.119.713.
Bangladesh (2013)7.021.817.914.910.650.4
Barbados (2010)6.520.520.412.27.699.2
Belarus (2013)2.420.813.
Belize (2010)
Benin (2016)17.926.317.811.024.373.6
Bhutan (2015)12.716.
Bolivia (2017)9.712.
Bosnia and Herzegovina (2013)2.822.716.
Botswana (2010)
Brazil (2009)
Bulgaria (2013)2.317.414.09.01.561.6
Burkina Faso (2009)
Burundi (2014)20.610.
Cabo Verde (2009)n.a.
Cambodia (2016)4.914.
Cameroon (2016)6.716.98.82.313.861.5
Central African Republic (2011)9.514.612.
Chad (2018)n.a.
Chile (2010)
China (2012)7.621.
Colombia (2017)12.511.58.53.518.674.8
Congo, Dem. Rep. (2013)
Congo, Rep. (2009)n.a.
Costa Rica (2010)
Côte d'Ivoire (2016)19.611.36.22.725.043.8
Croatia (2013)1.826.721.210.52.878.0
Czech Republic (2013)11.444.837.716.48.880.1
Djibouti (2013)10.421.
Dominica (2010)4.830.129.618.16.937.9
Dominican Republic (2016)
Ecuador (2017)
Egypt, Arab Rep. (2016)
El Salvador (2016)2.912.88.62.918.067.9
Eritrea (2009)
Estonia (2013)2.040.929.114.52.890.7
Eswatini (2016)4.114.610.52.24.562.2
Ethiopia (2015)
Fiji (2009)11.615.713.86.814.669.4
Gabon (2009)
Gambia, The (2018)4.812.
Georgia (2013)
Ghana (2013)7.817.38.03.814.868.5
Grenada (2010)9.312.
Guatemala (2017)
Guinea (2016)n.a.
Guinea-Bissau (2006)
Guyana, CR (2010)11.526.719.410.519.281.8
Honduras (2016)3.810.65.82.619.382.3
Hungary (2013)3.718.
India (2014)
Indonesia (2015)8.310.06.43.513.78.8
Iraq (2011)
Israel (2013)4.614.
Jamaica (2010)
Jordan (2013)4.631.525.010.95.358.1
Kazakhstan (2013)
Kenya (2013)10.332.821.210.919.553.1
Kosovo (2013)1.220.413.
Kyrgyz Republic (2013)7.217.310.45.511.869.2
Lao PDR (2016)
Latvia (2013)4.928.323.712.77.769.9
Lebanon (2013)4.939.129.611.39.773.7
Lesotho (2016)
Liberia (2017)
Lithuania (2013)1.428.821.811.73.862.7
Macedonia, FYR (2013)3.721.
Madagascar (2013)8.620.816.813.513.134.9
Malawi (2014)11.411.67.92.418.877.6
Malaysia (2015)6.319.411.25.07.645.4
Mali (2016)16.513.99.51.928.381.4
Mauritania (2014)26.023.518.415.326.773.1
Mauritius (2009)10.312.
Mexico (2010)
Micronesia, Fed. Sts. (2009)18.313.49.04.3n.a.77.8
Moldova (2013)
Mongolia (2013)
Montenegro (2013)2.914.
Morocco (2013)3.519.311.97.77.673.5
Mozambique (2007)
Myanmar (2016)
Namibia (2014)
Nepal (2013)
Nicaragua (2016)
Niger (2017)n.a.
Nigeria (2014)6.019.515.26.48.731.9
Pakistan (2013)11.418.012.67.912.727.1
Panama (2010)7.610.04.51.6n.a.28.2
Papua New Guinea (2015)n.a.
Paraguay (2017)
Peru (2017)8.415.412.05.514.079.9
Philippines (2015)
Poland (2013)4.620.916.16.34.455.2
Romania (2013)1.119.414.
Russian Federation (2012)
Rwanda (2011)
Samoa (2009)10.421.
Senegal (2014)
Serbia (2013)2.134.530.615.15.870.1
Sierra Leone (2017)n.a.
Slovak Republic (2013)1.733.024.311.95.263.4
Slovenia (2013)4.639.238.618.45.275.9
Solomon Islands (2015)6.316.912.38.813.686.0
South Africa (2007)4.513.
South Sudan (2014)n.a.
Sri Lanka (2011)
St. Kitts and Nevis (2010)6.719.412.67.36.9100.0
St. Lucia (2010)4.924.923.410.37.755.9
St. Vincent and the Grenadines (2010)4.218.913.17.87.681.7
Sudan (2014)n.a.
Suriname (2010)12.612.211.74.1n.a.52.1
Sweden (2014)2.420.
Tajikistan (2013)5.915.
Tanzania (2013)12.411.96.02.331.562.8
Thailand (2016)
Timor-Leste (2015)6.338.636.830.18.556.4
Togo (2016)8.232.526.416.018.981.0
Tonga (2009)
Trinidad and Tobago (2010)7.314.712.
Tunisia (2013)3.038.330.216.37.475.3
Turkey (2013)6.232.419.611.16.330.6
Uganda (2013)
Ukraine (2013)
Uruguay (2017)2.121.610.76.711.494.9
Uzbekistan (2013)
Vanuatu (2009)n.a.
Venezuela, R.B. (2010)
Vietnam (2015)6.912.
West Bank and Gaza (2013)2.526.722.413.417.075.8
Yemen, Rep. (2013)
Zambia (2013)10.711.
Zimbabwe (2016)
  • Notes

    * This indicator is computed using data from manufacturing firms only.

    Additional Notes

    1. Most surveys were administered using the Enterprise Surveys Global Methodology as outlined in the Methodology page, while some others did not strictly adhere to the Enterprise Surveys Global Methodology. For example, for surveys which do not follow the Global Methodology, the Universe under consideration may have consisted of only manufacturing firms or the questionnaire used may have been different from the standard global questionnaire. Data users should exercise caution when comparing raw data and point estimates between surveys that did and did not adhere to the Enterprise Surveys Global Methodology. For surveys which did not adhere to the Global Methodology plus Afghanistan 2008, any inference from one of these surveys is representative only for the data sample itself.
    2. Regional and "all countries" averages of indicators are computed by taking a simple average of country-level point estimates. For each economy, only the latest available year of survey data is used in this computation. Only surveys, posted during the years 2010-2017, and adhering to the Enterprise Surveys Global Methodology are used to compute these regional and "all countries" averages.
    3. Descriptions of firm subgroup levels, e.g. how the ex post groupings are constructed, are provided in the Indicator Descriptions (PDF, 710KB) document.
    4. Statistics derived from less than or equal to five firms are displayed with an "n.a." to maintain confidentiality and should be distinguished from ".." which indicates missing values. Also note for three growth-related indicators under the "Performance" topic, these indicators are not computed when they are derived from less than 30 firms.
    5. Standard errors are labeled "n.c.", meaning not computed, for the following:

           1) indicators for all surveys that were not conducted using the Enterprise Surveys Global Methodology and

           2) for indicator breakdowns by ex post groupings: exporter or ownership type, and gender of the top manager.
    6. Please cite our data as follows:

      Enterprise Surveys (, The World Bank.